import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)

class        UpperCamelCase						(				unittest.TestCase				):





						def __init__(self			,     __UpperCamelCase			,     __UpperCamelCase=13			,     __UpperCamelCase=7			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=True			,     __UpperCamelCase=99			,     __UpperCamelCase=32			,     __UpperCamelCase=5			,     __UpperCamelCase=4			,     __UpperCamelCase=37			,     __UpperCamelCase="gelu"			,     __UpperCamelCase=0.1			,     __UpperCamelCase=0.1			,     __UpperCamelCase=512			,     __UpperCamelCase=16			,     __UpperCamelCase=2			,     __UpperCamelCase=0.02			,     __UpperCamelCase=4			,     )				->				List[str]:
												UpperCamelCase_		:							List[str]  =		parent
												UpperCamelCase_		:							str  =		batch_size
												UpperCamelCase_		:							Tuple  =		seq_length
												UpperCamelCase_		:							List[str]  =		is_training
												UpperCamelCase_		:							str  =		use_attention_mask
												UpperCamelCase_		:							Any  =		use_token_type_ids
												UpperCamelCase_		:							Union[str, Any]  =		use_labels
												UpperCamelCase_		:							Optional[int]  =		vocab_size
												UpperCamelCase_		:							Union[str, Any]  =		hidden_size
												UpperCamelCase_		:							str  =		num_hidden_layers
												UpperCamelCase_		:							List[Any]  =		num_attention_heads
												UpperCamelCase_		:							Optional[int]  =		intermediate_size
												UpperCamelCase_		:							int  =		hidden_act
												UpperCamelCase_		:							Optional[int]  =		hidden_dropout_prob
												UpperCamelCase_		:							List[str]  =		attention_probs_dropout_prob
												UpperCamelCase_		:							List[Any]  =		max_position_embeddings
												UpperCamelCase_		:							str  =		type_vocab_size
												UpperCamelCase_		:							Dict  =		type_sequence_label_size
												UpperCamelCase_		:							Any  =		initializer_range
												UpperCamelCase_		:							Union[str, Any]  =		num_choices





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Union[str, Any]  =		ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size						)

												UpperCamelCase_		:							Union[str, Any]  =		None
												if self.use_attention_mask:
																		UpperCamelCase_		:							Any  =		random_attention_mask([self.batch_size, self.seq_length]						)

												UpperCamelCase_		:							Union[str, Any]  =		None
												if self.use_token_type_ids:
																		UpperCamelCase_		:							int  =		ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size						)

												UpperCamelCase_		:							str  =		RobertaConfig(
												    vocab_size=self.vocab_size			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     type_vocab_size=self.type_vocab_size			,     is_decoder=snake_case__			,     initializer_range=self.initializer_range			,     )

												return config, input_ids, token_type_ids, attention_mask





						def        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							Tuple  =		self.prepare_config_and_inputs()
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Any  =		config_and_inputs
												UpperCamelCase_		:							Dict  =		{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
												return config, inputs_dict





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							List[Any]  =		self.prepare_config_and_inputs()
												UpperCamelCase_,UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							int  =		config_and_inputs

												UpperCamelCase_		:							int  =		True
												UpperCamelCase_		:							int  =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
												UpperCamelCase_		:							Tuple  =		ids_tensor([self.batch_size, self.seq_length]			,     vocab_size=2						)

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)


@require_flax
class        UpperCamelCase						(				SCREAMING_SNAKE_CASE_	,	unittest.TestCase				):
						a__		:Tuple      =			True

						a__		:Optional[Any]      =			(
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def        A_							(self						)				->				List[Any]:
												UpperCamelCase_		:							str  =		FlaxRobertaModelTester(self						)





						@slow
						def        A_							(self						)				->				Optional[int]:
												for model_class_name in self.all_model_classes:
																		UpperCamelCase_		:							str  =		model_class_name.from_pretrained("""roberta-base"""			,     from_pt=snake_case__						)
																		UpperCamelCase_		:							int  =		model(np.ones((1, 1)						)						)
																		self.assertIsNotNone(snake_case__						)







from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
				A						=			input('Enter image url: ').strip()
				print(F'''Downloading image from {url} ...''')
				A						=			BeautifulSoup(requests.get(url).content, 'html.parser')
				# The image URL is in the content field of the first meta tag with property og:image
				A						=			soup.find('meta', {'property': 'og:image'})['content']
				A						=			requests.get(image_url).content
				A						=			F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
				with open(file_name, 'wb') as fp:
								fp.write(image_data)
				print(F'''Done. Image saved to disk as {file_name}.''')